Citation: Wang, S.; Su, F.; Ye, L.; Jing,
Y. Disinformation: A Bibliometric
Review. Int. J. Environ. Res. Public
Health 2022, 19, 16849. https://
doi.org/10.3390/ijerph192416849
Academic Editors: Josep
Vidal-Alaball and Wasim Ahmed
Received: 17 November 2022
Accepted: 13 December 2022
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International Journal of
Environmental Research
and Public Health
Article
Disinformation: A Bibliometric Review
Shixiong Wang
1
, Fangfang Su
2
, Lu Ye
2
and Yuan Jing
1,
*
1
Library, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
College of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China
* Correspondence: jingyuan@zstu.edu.cn
Abstract:
Objectives: This paper aimed to provide a systematic review of relevant articles from the
perspectives of literature distribution, research hotspots, and existing results to obtain the frontier di-
rections in the field of disinformation. Methods: We analyzed disinformation publications published
between 2002 and 2021 using bibliometric methods based on the Web of Science. There were 5666 pa-
pers analyzed using Derwent Data Analyzer (DDA). Results: The result shows that the USA was the
most influential country in this area, while Ecker and Lewandowsky from the University of Western
Australia published the largest volumes of papers. Keywords such as “social media”, “COVID-19”,
and “vaccination” have gained immense popularity recently. Conclusions: We summarized four
themes that are of the biggest concern to scholars: group heterogeneity of misinformation in memory,
disinformation mechanism in social media, public health related to COVID-19, and application of big
data technology in the infodemic. The future agenda of disinformation is summarized from three
aspects: the mechanism of disinformation, social media users, and the application of algorithms. This
work can be a meaningful resource for researchers’ study in the area of disinformation.
Keywords: disinformation; bibliometric analysis; keywords analysis; hot topics
1. Introduction
Disinformation is non-accidentally misleading information [
1
]. It will do direct or indi-
rect harm to people in venture capital [
2
,
3
], medical treatment [
4
–
7
], public opinion [
8
–
11
],
and even political communication [
12
–
17
]. Consequently, it becomes extremely significant
to review research relevant to disinformation. Prototypical varieties about disinformation
are false information [
18
], misinformation [
19
], and information pollution [
20
]. To avoid a
too broad or narrow definition, this paper focuses on the research process of disinformation.
Since the concept of “disinformation” was coined in the 1980s [
21
], many researchers
have conducted rigorous scientific research from different perspectives. From a political
perspective, disinformation has been instrumentalized, such as the function of misinforma-
tion during the election [
22
], and the dissemination of political disinformation on social
media [
23
]. From an economic perspective, scholars discussed the negative effects of the
diffusion of disinformation in the market, such as illegal huge profits [
24
], and deterioration
of market liquidity [
25
]. From a social perspective, disinformation in social media has
been a hot issue. Scholars used different criteria to screen the information on the Internet
and found that a large amount of medical misinformation was misleading people to make
wrong decisions. The misinformation was all about cancer [
26
], plastic surgery [
27
], vasec-
tomy [
28
], prehospital care [
29
], and vaccines [
30
]. From a military perspective, researchers
studied the application of disinformation in warfare in the context of the Cold War [
21
].
From an individual perspective, researchers studied the effects of automatic and intentional
memory [
31
], representation ability [
32
], drinking [
33
], and other factors on people’s inter-
ference by misinformation through experimental methods in the 2000s. Several documents
have provided suggestions for the prevention and control of disinformation. Some of these
documents focused on theory: Huang et al. explored random and targeted immunization
strategies and targeted immunization strategies [
34
]; Goslin et al. constructed website
Int. J. Environ. Res. Public Health 2022, 19, 16849. https://doi.org/10.3390/ijerph192416849 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 16849 2 of 21
quality assessment models [
35
,
36
]; Nguyen et al. found influential nodes in the dissemina-
tion network to contain the spread of misinformation [
37
]. Some documents focused on
practical applications: Fortinskyet et al. developed programs that provide services for rec-
ommending reliable resources [
38
]; Littman et al. put forward that education tools should
be developed for providing accurate information [
39
];
Lin et al.
selected a set of observers
to spot disinformation in time before being disseminated widely [
40
]. Overall, the research
on disinformation presents the characteristics of wide fields and diverse perspectives.
It is necessary to systematically analyze and summarize the research on disinforma-
tion from an overall perspective. Some scholars have made related reviews on the study
of disinformation. Their work focused on the definition and identification of disinfor-
mation [
41
,
42
], the key research directions of disinformation [
43
,
44
], the introduction of
different forms of disinformation [
41
,
45
], and the generalization of technologies to deal
with disinformation [
45
]. These reviews summarized the theoretical knowledge of disinfor-
mation and synthesized the findings to some extent. However, systematic review tends
to be dependent on qualitative analysis, which is inevitably affected by the intellectual
background of researchers and causes deviations in conclusions [46].
Quantitative analysis methods can avoid the aforementioned bias. Bibliometric analy-
sis and meta-analysis are two frequently used review alternatives that rely on quantitative
methods [
47
]. Meta-analysis is more suitable for similar literature and open issues [
48
].
Based on the characteristics of the large-scale and variety of literature in this study [
49
], this
paper endeavored to use bibliometric analysis to systematically summarize the whole pic-
ture of disinformation research from the number of papers, citation frequency, influencing
factors, H-index, and other dimensions.
It is generally believed that the earliest bibliometric research started in the early 20th
century. In the early stage of development, bibliometric methods were mostly used in
medical fields [
50
]. Around the 1940s, the establishment and maturity of Brad Ford’s Law,
Lotka’s Law, and Zipf’s Law laid a solid foundation for the development of bibliomet-
rics [
51
–
53
]. Since then, this method has been widely used in natural sciences [
54
,
55
],
subject areas like mathematics [56], physics [57], and chemistry [58].
There are a few papers that provide a bibliometric review of the concepts related to
disinformation. They provide an overview in some perspectives. Lee used bibliometric
methods to reveal the evolution of academic networks in the field of misinformation, but
his study was limited to the period 2009–2018, which clearly did not reflect the latest devel-
opments [
59
]. Some scholars reviewed rumors, fake news, and information epidemics, but
these are related concepts of disinformation, narrowing the scope of what disinformation
can accommodate [
60
–
62
]. The review of Tito et al. and Yeung et al. limited disinformation
to the social media [
63
] and medical fields [
64
], respectively, and although these two fields
are the main sites of disinformation, they limit the grasp of the filed in its entirety. The
review of Patra et al. summarized the existing results comprehensively to some extent,
but rarely tapped into research hotspots and research trends that are directly related to
disinformation [
65
]. Taken collectively, a comprehensive and objective analysis of the field
of disinformation and an overview of the frontiers of development are lacking. This paper
provides a systematic review as follows. First, the current status of research on this topic in
various countries around the world is sorted out and analyzed from temporal and spatial
perspectives. Second, based on the highly cited papers and hot papers, we provide an
overview of the existing results in terms of theories, methods, and conclusions. Third, the
hot topics in the field are explored, and the research frontiers are summarized based on the
evolution of research objects and topics.
The remainder of this paper is divided into the following sections: methods and
materials, results, discussion and expansion of new issues, and a summary of the full text.
2. Materials and Methods
The primary research method in this study is bibliometric analysis. It is not only
a quantitative and qualitative analysis of the dissemination of scientific literature using
Int. J. Environ. Res. Public Health 2022, 19, 16849 3 of 21
mathematical and statistical methods [
66
], but also a powerful tool for summarizing existing
knowledge structures and quantifying global scientific productivity in a specific field [
67
].
Bibliometric analysis usually consists of two parts: performance analysis and science
mapping [
68
]. Performance analysis contributes to help discover emerging themes and
recent advances in a field, the influence of leading scholars, and the impact of different
journals and schools of thought [
69
]. This paper uses performance analysis to find leading
countries/regions and journals, as well as prolific authors and institutions.
Bibliometric analysis relies on citation and co-citation analysis for quantitative re-
view [
70
]. Citations indicate the use of a specific work by a citing scholar and reveal the
value, importance, and influence of that work [
69
]. In this analysis, the most cited works
are used to illuminate the theoretical underpinnings, methodologies, and key themes that
drive the discipline in the field of disinformation.
Keyword co-occurrence analysis is a bibliometric method, which assumes that when
two keywords appear in multiple articles at the same time, there must be some correlation
between the concepts reflected [
71
]. It is considered appropriate to express central themes
in some fields using keywords [
72
]. Based on the numerous examples of literature, keyword
co-occurrence analysis is used to identify the central topics in disinformation.
With the advancement of bibliometric research, several analysis and visualization tools
have been developed, such as VOSviewer, Citespace, and Derwent Data Analyzer (DDA).
Among the tools, DDA is a more competitive software for cleaning, mining, and visualizing
patent data and scientific literature. DDA can analyze and track scientific research activities
in a particular research field. Hence, this study used the DDA 10 to present analyzing
results in the form of charts and tables in this research.
Our work is based on the Science Citation Index-Expanded (SCI-E) and the Social
Science Citation Index (SSCI) on the Web of Science. The retrieval formula is disinformation
or misinformation or “fake news” or “infodemic” or “information pollut *” or deepfake * or
“rumor propagation”. All the keywords were separated by an ‘or’ for further inclusivity [
73
].
The retrieval time is 10 January 2022. Results are restricted to the topic (title, keywords,
and abstract) and time-restricted to 1990.
In addition, there are some variables and metrological indicators used in this paper as
the following explanations:
TP: total publications.
TC: total citations of publications.
IF: impact factor of some journals in 2021.
ACPP: average citations per paper.
h-index: an indicator to describe the scientific productivity of researchers, representing
that at most h papers have been cited at least h times.
3. Results
According to the retrieval criteria, a total of 7326 papers were obtained. Only papers
in English belonging to “article” and “review” were screened. After removing papers
from years with low number of papers (1947–2001), papers other than articles and reviews,
papers published in languages other than English, and some non-closely related articles,
5666 related papers published from 2002 to 2021 were obtained. Three file types were
involved: articles (n = 4976), reviews (n = 367), and other types (n = 323). The average
citation frequency per paper for these papers was 50.97, and the total citation frequency
was 299,681.
3.1. Countries/Regions Production and Collaboration
A total of 139 countries/regions were involved in the scientific research production of
disinformation during 2002–2021. Figure 1 shows the growth of disinformation research
papers and the top 20 countries/regions with the highest productivity. The number of
papers related to disinformation has increased exponentially year by year. The USA, the UK,
China, and Australia rank in the top four, according to citation frequency. Concretely, the
Int. J. Environ. Res. Public Health 2022, 19, 16849 4 of 21
number of disinformation-related papers published in the top 20 countries has increased
over time, especially after 2019, when most countries such as the USA, the UK, and Australia
achieved a surge. The articles published by the USA account for half of the top 20 countries,
even amounting to 65.7% (2004). It must be stressed that although the number of papers
published by the USA in the field has increased year by year, its percentage has had a
significant downward trend.
–
Figure 1. Number of yearly papers in the top 20 high-yield countries/regions.
Figure 2 shows the collaboration among the top 20 countries/regions in terms of the
number of papers and the specific frequency of collaboration, with the nodes representing
countries/regions, the size of a node representing the number of journal papers published
in that country/region, and the straight line between two dots indicating the collaboration
generated between the countries/regions.
Productive countries collaborate more with other countries, and they even have coopera-
tion with each of the top 20 countries/regions, such as the USA, the UK, Australia, and China.
The USA has closer collaborations with the UK (164 papers), Australia (102 articles), and China
(102 articles). Except for the USA, the UK has the highest frequency of collaboration with
Australia (85 papers) and The Netherlands (40 articles). The intensity of China’s partnerships
with other countries is generally low. Besides the USA, China has the highest frequency of
collaboration with Australia, but only 30 papers have been published. It should be underlined
that some of the top 20 countries/regions have not jointly published papers yet, such as
South Korea, New Zealand, and Poland.
Int. J. Environ. Res. Public Health 2022, 19, 16849 5 of 21
’
’
Figure 2. Cooperation network map of the top 20 high-yield countries/regions.
3.2. The Most Attractive Journals
A total of 2116 journals published articles on disinformation research, among which
1252 publications published only one paper. The top 30 academic journals with the number
of articles are listed in Table 1. These journals published 22.86% of the total number
of papers. The top three academic journals are APPLIED COGNITIVE PSYCHOLOGY,
PLOS ONE, and JOURNAL OF MEDICAL INTERNET RESEARCH. Specifically, there are
11 journals from the USA and eight from the UK, which accounts for 63.3% of the top 30. It
should be noted that MEDIA COMMUN-LISBON comes from Portugal, which is not one
of the top 20 high-yield countries/regions. In addition, these 30 journals generally have a
high value of IF, with an average of 4.08. The journal with the highest IF is P NATL ACAD
SCI USA, which published 38 papers. For further analysis, based on the characteristics
of these journals’ publication volume over time, they can be divided into the following
categories: firstly, the main journals represented by APPLIED COGNITIVE PSYCHOLOGY.
Its relevant papers are produced every year from 2002 to 2021, and the number of papers
published each year is relatively stable. Next are the rising stars represented by IEEE
ACCESS, SOCIAL MEDIA AND SOCIETY, and DIGITAL JOURNALISM. They have a clear
trend of growth in the number of papers published in recent years, especially PLOS ONE
and JOURNAL OF MEDICAL INTERNET RESEARCH who have published the largest
number of papers in the past two years. Finally, there are some journals with potential.
Take CONTRACEPTION, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES
OF THE UNITED STATES OF AME as an example; it has been focusing on the field of
disinformation, but the number of publications per year is not high.
Int. J. Environ. Res. Public Health 2022, 19, 16849 6 of 21
Table 1. The top 30 most attractive journals.
Rank Publication TP Category Countries/Regions IF
1 APPL COGNITIVE PSYCH 118 Psychology, Experimental USA 2.36
2 PLOS ONE 113 Multidisciplinary Sciences USA 3.75
3 J MED INTERNET RES 102
Medical Informatics, Health Care
Sciences & Services
Canada 7.08
4 INT J ENV RES PUB HE 83 Public, Environmental & Occupational Health Switzerland 4.61
5 MEMORY 78 Psychology, Experimental UK 2.52
6 IEEE ACCESS 62
Telecommunications, Engineering, Electrical &
Electronic, Computer Science,
Information Systems
USA 3.48
7 SOC MEDIA SOC 60 Communication UK 4.65
8 DIGIT JOURNAL 45 Communication UK 6.85
9 NEW MEDIA SOC 43 Communication USA 5.31
10 INT J COMMUN-US 40 Communication USA 1.64
11 PHYSICA A 40 Physics, Multidisciplinary The Netherlands 3.78
12 P NATL ACAD SCI USA 38 Multidisciplinary Sciences USA
12.78
13 HEALTH COMMUN 37 Communication, Health Policy & Services USA 3.50
14 MEM COGNITION 36 Psychology, Experimental USA 2.48
15 FRONT PSYCHOL 35 Psychology, Multidisciplinary Switzerland 4.23
16 INFORM COMMUN SOC 35 Communication, Sociology UK 5.05
17 JOURNAL PRACT 31 Communication UK 2.33
18 MEDIA COMMUN-LISBON 31 Communication Portugal 3.04
19 VACCINE 31
Medicine, Research & Experimental, Immunology
The Netherlands 4.17
20 BMC PUBLIC HEALTH 29 Public, Environmental & Occupational Health UK 4.14
21 VACCINES-BASEL 28
Medicine, Research & Experimental, Immunology
Switzerland 4.96
22
INFORM PROCESS MANAGE
27 Information Science & Library Science UK 7.47
23 J APPL RES MEM COGN 27 Psychology, Experimental The Netherlands 4.6
24 JOURNALISM 26 Communication USA 3.19
25 SCI REP-UK 26 Multidisciplinary Sciences UK 4.99
26 AM BEHAV SCI 25
Psychology, Clinical, Social
Sciences, Interdisciplinary
USA 2.53
27 FRONT PUBLIC HEALTH 25 Public, Environmental & Occupational Health Switzerland 6.46
28 JMIR PUBLIC HLTH SUR 25 Public, Environmental & Occupational Health Canada 4.11
29 CONTRACEPTION 24 Obstetrics & Gynecology The Netherlands 3.05
30
HUM VACC
IMMUNOTHER
24
Biotechnology & Applied
Microbiology, Immunology
USA 4.53
3.3. Leading Authors
A total of 17,661 authors participates in the study of disinformation. Table 2 shows
the top 30 high-yield authors. The authors are spread out across 10 countries/regions and
26 institutions. The most productive contributors in the field of disinformation are Ecker
(TP: 37) and Lewandowsky (TP: 37) from the University of Western Australia, Australia,
followed by Loftus (TP: 36) from Univ Calif Irvine in the USA. Among them, Lewandowsky
has the highest value in the H-index and TC. His findings have been cited 3063 times in
total, and the value of the H-index is up to 21, indicating that papers reported by him have
a high impact in the field of disinformation. Although Reifler published only 11 papers
relevant to disinformation, he ranks first in ACPP, demonstrating the high quality of his
academic output. Additionally, consistent with the previous analysis, most of these authors
come from the most productive countries/regions, like the USA (n = 10), Australia (n = 4),
The Netherlands (n = 4), and the UK (n = 3). As one of the most productive countries in the
field, China has only one author in the top 30 (Zhu from Jiangsu University).
Int. J. Environ. Res. Public Health 2022, 19, 16849 7 of 21
Table 2. The top 30 authors regarding disinformation research from 2002 to 2021.
Rank Author TP TC ACPP H-Index Institution Countries/Region
1 Ecker, UKH 37 2468 66.70 18 Univ Western Australia Australia
2 Lewandowsky, S 37 3063 82.78 21 Univ Western Australia Australia
3 Loftus, EF 36 1477 41.03 17 Univ Calif Irvine USA
4 Otgaar, H 26 249 9.58 9 Maastricht Univ The Netherlands
5 Pennycook, G 20 1409 70.45 12 Univ Regina Canada
6 Zhu, Linhe 20 228 11.40 9 Jiangsu Univ China
7 Paterson, HM 19 337 17.74 8 Univ Sydney Australia
8 Rand, DG 19 1409 74.16 12 MIT USA
9 Vraga, EK 19 756 39.79 11 Univ Minnesota USA
10 Nyhan, B 17 2269 133.47 12 Dartmouth Coll USA
11 Garry, M 16 463 28.94 12 Victoria Univ Wellington New Zealand
12 Luna, K 16 175 10.94 8 Univ Minho Portugal
13 van der Linden, S 16 712 44.50 11 Univ Cambridge UK
14 Wright, DB 16 1016 63.50 16 Florida Int Univ USA
15 Chan, JCK 15 381 25.40 10 Iowa State Univ USA
16 Merckelbach, H 15 217 14.47 9 Maastricht Univ The Netherlands
17 Bode, L 14 581 41.50 8 Georgetown Univ USA
18 Cook, J 14 2003 143.07 10 George Mason Univ USA
19 Quattrociocchi, W 14 1494 106.71 11 Ca Foscari Univ Venice Italy
20 Polczyk, R 13 36 2.77 4 Jagiellonian Univ Poland
21 Thomas, AK 13 236 18.15 6 Tufts Univ USA
22 Zollo, F 13 1250 96.15 10 Ca Foscari Univ Venice Italy
23 Gabbert, F 12 956 79.67 11 Univ Portsmouth USA
24 Memon, A 12 899 74.92 10 Univ Aberdeen UK
25 Sauerland, M 12 78 6.50 5 Maastricht Univ The Netherlands
26 Szpitalak, M 12 30 2.50 3 Jagiellonian Univ Poland
27 Hameleers, M 11 111 10.09 5 Univ Amsterdam The Netherlands
28 Kemp, RI 11 298 27.09 8 UNSW Sydney Australia
29 Reifler, J 11 2092 190.18 9 Univ Exeter UK
30 Scala, A 11 1430 130.00 11 CNR ISC Italy
In addition, we analyzed the cooperation among the top 30 high-yield authors. As
shown in Figure 3, each rounded rectangle represents an author, the dot connected to the
rounded rectangle represents the number of published papers, and the dot connected to the
two rounded rectangles represents the number of papers jointly published by two authors.
1 Univ Washington 235 4761 20.26 USA
2 MIT 228 4392 19.26 USA
3 Boston Univ 178 4867 27.34 USA
4 Univ Cambridge 164 7053 43.01 UK
5 Univ N Carolina 154 2349 15.25 USA
6 Harvard Univ 143 4723 33.03 USA
7 Columbia Univ 129 2029 15.73 USA
8 Univ Penn 124 4472 36.06 USA
9 Univ Sydney 121 2134 17.64 Australia
10 Univ Michigan 113 6647 58.82 USA
11 Univ Oxford 83 996 12 UK
12 NYU 83 3084 37.16 USA
13 Univ Western Australia 76 3416 44.95 Australia
14 Univ Toronto 73 2413 33.05 Canada
15 Univ Wisconsin 73 1854 25.4 USA
16 Univ Illinois 72 2050 28.47 USA
Figure 3. DDA cluster diagram of cooperation among the top 30 authors.
Int. J. Environ. Res. Public Health 2022, 19, 16849 8 of 21
The cooperation among high-yield authors is characterized by overall dispersion and
localized intensity. In terms of breadth, there is more collaboration between Lewandowsky
and the other top 30 authors, involving three countries: the UK, the USA, and Australia.
Simultaneously, the relationship among Cook, Ecker, and van Der Linden is also typical
multilateral cooperation. In terms of depth, the cooperative relationship between Pennycook
and Rand is the most intimate, as they have cooperated 18 times. Other stable cooperative
relationships include the cooperation between Bode and Varga, as well as the cooperation
among Zollo, Quattrociocchi, and Scala. It is worth noting that Loftus and Zhu, who published
many papers, have not cooperated with other top 30 most productive authors.
3.4. Leading Institutions
A total of 4888 institutions have participated in the study of disinformation. Table 3
shows the top 30 research institutions with the highest productivity, including 22 from the
USA, three from the UK, two from Australia, and one each from Singapore, Canada, and
The Netherlands. Among them, the total number of papers published by Univ Washington
ranks first, followed by MIT and Boston Univ. Papers produced by Univ Cambridge have
the highest citations, followed by Univ Michigan and Boston Univ. Alternatively, Univ
Michigan ranks first in ACPP, followed by Stanford Univ and Univ Western Australia.
Table 3. The top 30 institutions with the highest productivity.
Rank Institutions TP TC ACPP Countries/Regions
1 Univ Washington 235 4761 20.26 USA
2 MIT 228 4392 19.26 USA
3 Boston Univ 178 4867 27.34 USA
4 Univ Cambridge 164 7053 43.01 UK
5 Univ N Carolina 154 2349 15.25 USA
6 Harvard Univ 143 4723 33.03 USA
7 Columbia Univ 129 2029 15.73 USA
8 Univ Penn 124 4472 36.06 USA
9 Univ Sydney 121 2134 17.64 Australia
10 Univ Michigan 113 6647 58.82 USA
11 Univ Oxford 83 996 12 UK
12 NYU 83 3084 37.16 USA
13 Univ Western Australia 76 3416 44.95 Australia
14 Univ Toronto 73 2413 33.05 Canada
15 Univ Wisconsin 73 1854 25.4 USA
16 Univ Illinois 72 2050 28.47 USA
17 Yale Univ 62 1938 31.26 USA
18 Univ Calif Irvine 60 1952 32.53 USA
19 Univ Bristol 57 1853 32.51 UK
20 Univ Maryland 56 664 11.86 USA
21 Univ Minnesota 55 695 12.64 USA
22 Duke Univ 55 1193 21.69 USA
23 Univ Calif San Francisco 54 603 11.17 USA
24 Maastricht Univ 50 511 10.22 The Netherlands
25 Stanford Univ 49 2298 46.9 USA
26 Northwestern Univ 48 804 16.75 USA
27 Nanyang Technol Univ 46 1108 24.09 Singapore
28 Univ Texas Austin 45 572 12.71 USA
29 Arizona State Univ 44 643 14.61 USA
30 Ohio State Univ 43 923 21.47 USA
In addition, this paper analyzed the cooperation in disinformation research among
the top 30 high-yield research institutions. As shown in Figure
4, each node represents
an organization. The line between nodes represents the cooperation relationship between
organizations. The thickness of the line indicates the frequency of cooperation. The thicker
the line, the closer the cooperation relationship between the two.
Int. J. Environ. Res. Public Health 2022, 19, 16849 9 of 21
17 Yale Univ 62 1938 31.26 USA
18 Univ Calif Irvine 60 1952 32.53 USA
19 Univ Bristol 57 1853 32.51 UK
20 Univ Maryland 56 664 11.86 USA
21 Univ Minnesota 55 695 12.64 USA
22 Duke Univ 55 1193 21.69 USA
23 Univ Calif San Francisco 54 603 11.17 USA
24 Maastricht Univ 50 511 10.22 The Netherlands
25 Stanford Univ 49 2298 46.9 USA
26 Northwestern Univ 48 804 16.75 USA
27 Nanyang Technol Univ 46 1108 24.09 Singapore
28 Univ Texas Austin 45 572 12.71 USA
29 Arizona State Univ 44 643 14.61 USA
30 Ohio State Univ 43 923 21.47 USA
Figure 4. Cooperation cluster diagram of the top 30 institutions with the highest productivity.
It may be concluded that the top 30 institutions show close or sparse cooperation
with each other. The higher-ranking institutions show stronger cooperation ability, and
among them, Harvard Univ has the widest cooperation range. It has cooperated with
25 institutions in disinformation, published five relevant papers with MIT, cooperated
with Yale Univ and Univ N Carolina four times, and cooperated with Univ Sydney, Univ
Penn, and Univ California Irvine three times. The Univ N Carolina has partnerships with
21 institutions, such as the Univ Penn, the Univ Calif San Francisco, and Boston Univ. In
the cooperative network, the relationship between Univ Bristol and Univ Western Australia
is the most stable (n = 24), and the other strong cooperative relationships are Duke Univ
and Univ N Carolina (n = 9), Boston Univ and Univ N Carolina (n = 5). However, some
institutions such as Univ Toronto, Univ Washington, and Arizona State Univ have not
cooperated with other top 30 institutions.
3.5. Keywords Analysis
By analyzing the keywords, we can understand the key fields of disinformation
research. Therefore, this research conducted a statistical analysis on the keywords of
5666 papers. After cleaning the data, the visualized data are shown in Figure
5. They
show the bubble chart of the top 30 high-frequency keywords over time, which uses
three-dimensional data to explain the changing trend of disinformation research. The first
dimension is time, which spans from 2002 to 2021. The second dimension is the total
frequency of every keyword, and the higher the ranking, the higher the frequency. The
third dimension is the frequency of some keywords in some years; the size of the bubble
will change with the occurrence frequency of the keyword in that year.
Int. J. Environ. Res. Public Health 2022, 19, 16849 10 of 21
1
Social Media and Fake News in
the 2016 Election
partisan bias, polarization, online,
accuracy, beliefs, impact
J ECON PER-
SPECT
1043 USA
2
Misinformation and Its Correc-
tion: Continued Influence and
Successful Debiasing
misinformation, false beliefs,
memory updating, debiasing
PSYCHOL SCI
PUBL INT
921 USA, Australia
Figure 5. Annual variation bubble chart of disinformation research keywords.
“Misinformation” has the highest frequency (n = 817), followed by “social media”
(
n = 684
) and “COVID-19” (n = 600). There are some differences in the research process of
these keywords. They can be divided into three categories according to the characteristic of
Int. J. Environ. Res. Public Health 2022, 19, 16849 11 of 21
keywords with time variation. The first kind possesses continuous popularity, including
“false memory”, “misinformation effect”, and “suggestibility”. The bubble chart of such
keywords almost fills the entire time interval, and the bubbles are relatively large, with-
out obvious changes over time. The second kind is those keywords produced in recent
10 years, such as “Machine Learning”, “Deep Learning”, “Natural Language Processing”,
“Facebook”, and “infodemiology”. Such keywords indicate researchers have widely used
intelligent algorithms to identify and classify disinformation on social media and study
its mechanism [
74
–
76
]. The last kind is keywords of rising popularity, which will appear
sooner or later. Since 2017, the size of bubbles belonging to this kind of keyword has shown
a trend of linear growth. They are “misinformation”, “social media”, “fake news”, and
“public health”.
3.6. Highly Cited Papers
There are 176 most cited papers among the 5666 papers in disinformation. The top
20 highly cited papers are listed in Table
4. Focusing on the distribution of these papers,
35% of the papers are jointly published by two or more countries, and 65% of the papers
are from the USA.
Table 4. Top 20 papers highly cited in disinformation from 2002 to 2021.
Rank Title Keywords Journal TC Countries/Regions
1
Social Media and Fake News
in the 2016 Election
partisan bias, polarization,
online, accuracy, beliefs, impact
J ECON
PERSPECT
1043 USA
2
Misinformation and Its
Correction: Continued
Influence and
Successful Debiasing
misinformation, false beliefs,
memory updating, debiasing
PSYCHOL SCI
PUBL INT
921 USA, Australia
3
Why do humans reason?
Arguments for an
argumentative theory
argumentation, confirmation
bias, decision making, dual
process theory, evolutionary
psychology, motivated
reasoning, reason-based
choice, reasoning
BEHAV BRAIN SCI
806 USA, France
4
Opioid Epidemic in the
United States
opioid abuse, opioid misuse,
nonmedical use of
psychotherapeutic drugs,
nonmedical use of opioids,
national survey on drug use
and health, opioid guidelines
PAIN
PHYSICIAN
673 USA
5
The spreading of
misinformation online
misinformation, virality,
Facebook, rumor
spreading, cascades
P NATL ACAD SCI
USA
604 USA, Italy
6
Effective Messages in Vaccine
Promotion: A
Randomized Trial
vaccines, myths, rumor,
autism, false,
misperceptions, misinformation
PEDIATRICS 583 USA
7
DEFINING FAKE NEWS,
A typology of
scholarly definitions
facts, fake news, false news,
misinformation, news,
parody, satire
DIGIT
JOURNAL
502 Singapore
8
Anti-vaccine activists,
Web 2.0, and the postmodern
paradigm—An overview of
tactics and tropes used
online by the
anti-vaccination movement
anti-vaccination, health
communication, internet,
postmodernism, vaccines,
web 2.0
VACCINE 444 Canada
Int. J. Environ. Res. Public Health 2022, 19, 16849 12 of 21
Table 4. Cont.
Rank Title Keywords Journal TC Countries/Regions
9
Mind the Hype: A Critical
Evaluation and Prescriptive
Agenda for Research on
Mindfulness and Meditation
mindfulness, meditation,
psychotherapy, neuroimaging,
contemplative science,
adverse effects, media
hype, misinformation
PERSPECT
PSYCHOL SCI
440
Australia,
The Netherlands,
USA
10
Mental health problems and
social media exposure during
COVID-19 outbreak
PLOS ONE 413 China
11
Attitudes to vaccination: A
critical review
Europe, vaccination,
immunization, public health,
choice, attitude,
perception, hesitancy
SOC SCI MED 364 UK
12
The Effects of Anti-Vaccine
Conspiracy Theories on
Vaccination Intentions
continued influence, African
Americans, beliefs, attitudes,
misinformation, HIV/aids,
impact, online
PLOS ONE 362 UK
13
Vaccine hesitancy: the next
challenge in the fight
against COVID-19
COVID-19, SARS-CoV-2
vaccine, vaccine hesitancy,
healthcare staff, vaccine
safety, Israel
EUR J
EPIDEMIOL
338 Israel
14
Beyond Misinformation:
Understanding and Coping
with the Post-Truth Era
misinformation, fake
news, post-truth
politics, demagoguery
J APPL RES MEM
COGN
327
UK,
Australia,
USA
15
Opinion Dynamics and
Learning in Social Networks
Bayesian updating, consensus,
disagreement, learning,
misinformation, non-Bayesian
models, rule of thumb behavior,
social networks
DYN GAMES
APPL
320 USA
16
Lazy, not biased:
Susceptibility to partisan fake
news is better explained by
lack of reasoning than by
motivated reasoning
fake news, news media, social
media, analytic thinking,
cognitive reflection test,
intuition, dual process theory
COGNITION 306 USA
17
Fake news on Twitter
during the 2016 US
presidential election
SCIENCE 284 USA
18
Fighting COVID-19
Misinformation on social
media: Experimental
Evidence for a Scalable
Accuracy-Nudge Intervention
social media, decision making,
policy making, reflectiveness,
social cognition, open data,
open materials, preregistered
PSYCHOL SCI 283 Canada, USA
19
Motivational pathways to
STEM career choices: Using
expectancy-value perspective
to understand individual and
gender differences in
STEM fields
career choices, stem, individual
and gender differences,
expectancy-value theory
DEV REV 268 USA
20
NASA Faked the Moon
Landing-Therefore, (Climate)
Science Is a Hoax: An
Anatomy of the Motivated
Rejection of Science
scientific communication,
policymaking, climate science
PSYCHOL SCI 268
Australia,
Switzerland
This paragraph summarizes the research findings of some papers. In 2011, Acemoglu et al.
discussed the possibility that media sources, politicians, and the state could manipulate
misinformation [77]. In 2012, Lewandowsky et al. examined the mechanisms by which such
misinformation is disseminated in society and pointed out that works of fiction are also the
Int. J. Environ. Res. Public Health 2022, 19, 16849 13 of 21
source of misinformation [
78
]. Public consumers may be harmed, misled, and disappointed
by such misinformation [
79
]. Jolley et al. highlighted the potentially detrimental consequences
of anti-vaccine conspiracy theories in 2014 [
80
]. In the same year,
Nyhan et al.
tested the
effectiveness to reduce vaccine misperceptions of four interventions [
81
]. After the 2016
presidential election, the public has shown concerns about misinformation on social media [
82
].
Allcott and Gentzkow found social media was an important source of election news in
2016 [
83
]. Although there were many studies on the source and transmission mechanism of
misinformation, there was still a lack of perfect measures to reduce the harm of misinformation.
In 2020, Pennycook et al. showed participants were far worse at discerning between true and
false content, and the level of true discernment in participants’ subsequent sharing intentions
could be nearly tripled using a simple accuracy reminder [84].
3.7. Analysis of Hot Papers
Different from highly cited papers, hot papers represent the latest research directions.
Among the 5666 papers, 16 hot papers are shown in Table 5. Among them, there are three
review papers and 13 papers. From the time dimension, the 16 hot papers are concentrated
in 2019, 2020 and 2021. At this junction, COVID-19 broke out and spread rapidly. From the
spatial dimension, hot papers are distributed in four main countries, including seven in
the USA, six in the UK, two in The Netherlands, and one in Germany.
Table 5. Hot papers in disinformation research from 2002 to 2021.
Rank Title Keywords Journal TC Countries/Regions
1
Mental health problems and
social media exposure during
COVID-19 outbreak
PLOS ONE 413 China
2
Vaccine hesitancy: the next
challenge in the fight
against COVID-19
COVID-19, SARS-CoV-2
vaccine, Vaccine hesitancy,
Healthcare staff, Vaccine
safety, Israel
EUR J
EPIDEMIOL
338 Israel
3
Fighting COVID-19
misinformation on social
media: experimental
evidence for a scalable
accuracy-nudge intervention
social media, decision making,
policy making, reflectiveness,
social cognition, open data,
open materials, preregistered
PSYCHOL SCI 283 Canada, USA
4
Systematic literature review
on the spread of
health-related misinformation
on social media
Misinformation, Fake news,
Health, Social media
SOC SCI MED 244 UK, Italy
5
A comprehensive review of
the COVID-19 pandemic and
the role of IoT, Drones, Ai,
Blockchain, and 5G in
managing its impact
Coronavirus, COVID-19,
pandemic, transmission stages,
global economic impact, UAVs
for disaster management,
Blockchain, IoMT applications,
IoT, AI, 5G
IEEE
ACCESS
219 India, Qatar
6
The digital transformation of
innovation and
entrepreneurship: Progress,
challenges, and key themes
Digital transformation,
Innovation, Entrepreneurship,
Digital innovation, Digital
platforms, Openness,
Generativity, Affordance
RES
POLICY
191 USA, UK
7
Health-protective behavior,
social media usage, and
conspiracy belief during the
COVID-19 public
health emergency
Conspiracy beliefs, COVID-19,
health-protective behaviors,
public health, social media
PSYCHOL MED 168 UK
Int. J. Environ. Res. Public Health 2022, 19, 16849 14 of 21
Table 5. Cont.
Rank Title Keywords Journal TC Countries/Regions
8
Transmission of SARS-CoV-2:
a review of viral, host, and
environmental factors
attack rate, infections
ANN
INTERN MED
155 USA
9
Conspiracy theories as
barriers to controlling the
spread of COVID-19 in the US
Conspiracy theories, COVID-19,
Prevention, Vaccination,
Political ideology, Media use,
Vaccination misinformation
SOC SCI MED 152 USA
10
Social media and
vaccine hesitancy
vaccines
BMJ GLOB
HEALTH
108 USA, South Africa
11
Measuring the impact of
COVID-19 vaccine
misinformation on
vaccination intent in
the UK and USA
public-health, hesitancy, exposure,
opinion, news
NAT HUM BEHAV
107 USA, UK, Belgium
12
Considering emotion in
COVID-19 vaccine
communication: addressing
vaccine hesitancy and
fostering vaccine confidence
fear, misinformation,
metanalysis, appeals
HEALTH
COMMUN
104 USA
13
A survey on fake news and
rumor detection techniques
Fake news, Rumors, Natural
language processing,
Data mining, Text mining,
Classification, Machine learning,
Deep learning
INFORM
SCIENCES
90 Italy
14
Fact-checking as risk
communication: the
multi-layered risk of
misinformation in times
of COVID-19
Risk communication,
misinformation, trust, uncertainty
J RISK RES 79 USA, Germany
15
An incentive-aware
blockchain-based solution for
internet of fake media things
Blockchain, Fake news, Internet
of fake media things,
Proof-of-authority
INFORM
PROCESS MANAG
57
Canada, Taiwan
(China),
USA, Kuwait
16
When fear and
misinformation go viral:
Pharmacists’ role in deterring
medication misinformation
during the ‘infodemic’
surrounding COVID-19
Coronavirus, Misinformation,
COVID-19, Pandemics,
Pharmacists
RES SOC ADMIN
PHARM
55 Australia, Ethiopia
These hot papers mainly represent two popular topics. Some papers focus on the
negative influence of misinformation on social media during COVID-19. For instance,
Gao et al.
revealed the correlation between public mental health problems and misinfor-
mation exposure on social media [
85
]. Allington et al. proposed conspiracy theories are
the barriers to controlling the epidemic. Particularly, four papers underlined the impact
of vaccine misinformation in vaccine promotion [
86
–
89
]. The other papers introduced
measures to deter misinformation. Chen et al. put forward a solution based on blockchain
for fake news [
90
]. Erku et al. attached importance to pharmacists’ role in controlling
misinformation [
91
]. Most of these hot papers were produced based on COVID-19, which
proves that after the outbreak of COVID-19, scholars’ attention to disinformation has risen
to a high level.
4. Discussion
A total of 5666 papers were identified for the present bibliometric assessment of
research on disinformation in this study. The results of the study indicate that the number
of papers produced increased the most from 2019 to 2021. This change indicates that
the outbreak of COVID-19 has pushed disinformation research to a new climax and has
Int. J. Environ. Res. Public Health 2022, 19, 16849 15 of 21
considerably influenced the research orientation and hot areas of disinformation. Among
many countries/regions, the USA leads the field of disinformation research, with the
largest number of publications and the highest frequency of citations. This is attributed to
the manipulation of disinformation in many political events, providing the best breeding
context and sufficient cases for the USA to study disinformation, such as the Cold War
between the USA and the Soviet Union and the 2016 US presidential election. However, it
should be noted that the share of the USA in the world is decreasing year by year, which
indicates that the significance of disinformation research is beginning to penetrate other
countries/regions, such as Italy, The Netherlands, New Zealand, and other developed
countries. Moreover, the research in this field in developing countries is not very brilliant.
China ranks fourth in terms of number of publications; however, its collaboration with
other countries is not deep and extensive. As the only Chinese author in the top 30, Zhu
had no collaborations with other high-yield authors. The reasons for this are twofold. On
one hand, it is attributed to geographical differences. Physical distance prevents Chinese
authors from collaborating with other highly productive authors. On the other hand, there
is the lag of academic research. Zhu has been studying rumor propagation since 2016 and
has published 10 articles related to rumor propagation models since 2019. Starting later
than other scholars, other Chinese authors working with Zhu have not yet been able to
stand out. Therefore, developing countries, represented by China, still have a lot of room
for development.
The most prolific contributors to the field of disinformation are Ecker and Lewandowsky.
Lewandowsky engaged in research on the intrinsic link between disinformation dissemination
and cognitive behavior in 2005, while Ecker began to be engaged in related research later.
They have worked closely together and have jointly published highly cited articles such as
“Misinformation and Its Correction: Continued Influence and Successful Debiasing” and
“Beyond Misinformation: Understanding and Coping with the ‘Post-Truth’ Era”. During this
period, they conducted numerous experiments to investigate the mechanisms of misinforma-
tion transmission in society so as to reveal the effects of misinformation in memory work and
to outline options for dealing with misinformation in the post-truth era.
Based on the results of the previous keywords analysis, two turning points can be seen
to have affected scholars’ research on the key areas of disinformation: the emergence of
the world wide web with various social network sites and the outbreak of COVID-19, as
shown in Table 6. Before the emergence of social networking sites, the misinformation effect
was a hot issue in the field. The keywords with a high frequency of co-occurrence with
misinformation included age difference, memory, children, adults, etc. During this period,
scholars paid attention to the group heterogeneity of misinformation in memory [
92
–
95
].
Before the outbreak of COVID-19, the Internet was an important source of disinformation.
Keywords with high co-occurrence frequency with social media included media, fake
news, Facebook, and trust. Researchers were committed to studying the mechanism of
disinformation in social networks [
96
,
97
]. After the outbreak of COVID-19, researchers have
tended to explore the impact of disinformation on epidemic prevention and control and
public health [
98
]. The high-frequency keywords coexisting with COVID-19 are vaccine,
health, and medical treatment. In addition, at this stage, the topic of infodemic has become
hot, with the focus on the mixture of false information and true information after the
outbreak of infectious diseases [
99
,
100
]. Relevant research technologies include machine
learning [101,102], deep learning [103], and complex networks [104].
Evaluating disinformation research from a historical perspective is critical for mea-
suring the current and future impacts of disinformation. By scrutinizing key papers and
analyzing information about the authors’ countries/regions, institutions, disciplines, and
topics, we can present a portrait of misinformation research that will enable future scholars
to evaluate the direction of research.
Int. J. Environ. Res. Public Health 2022, 19, 16849 16 of 21
Table 6. Stages of misinformation research.
Stage Keywords Theme
Before the emergence of WWW
misinformation, age-difference, recall, memory,
children, adults, suggestibility, judgment,
memory conformity, false memory
group heterogeneity of
misinformation in memory
After the emergence of social networking
sites and before the outbreak
of COVID-19
social media, media, fake news, communication,
Facebook, credibility, trust, bias, disinformation,
journalism, truth
disinformation mechanism in
social media
After the outbreak of COVID-19
COVID-19, vaccination, health, management,
knowledge, prevalence, pandemic
public health related to
the COVID-19
infodemic, coronavirus, infodemiology,
crisis, infoveillance,
Twitter, machine learning, deep learning, social
networks, fake news detection, diffusion,
fact-checking, blogs, dynamics
application of big data technology
in infodemic
A comprehensive study of the countries/regions, institutions, publications, and au-
thors that have contributed most to disinformation research reveals that disinformation
research has long been centered in the USA and the UK. The centrality of the USA and the
UK is reflected in the quantity of high-impact papers, institutions, authors, and academic
journals. However, this dynamic is changing, with the rise of research powers from coun-
tries/regions such as China, Italy, and The Netherlands. If we bring the time from 2002
closer to the present, we can see that scholars from these countries/regions are increasingly
occupying the ranks of high-impact authors in the sequence. This trend is particularly
evident when we compare ESI Highly Cited Papers (with a 10-year statistical cycle) and
ESI Hot Papers (with a 3-year statistical cycle). We can see that ESI highly cited papers
mainly come from predominant countries such as the UK and the USA, but the first place
among ESI hot papers has become the work of Chinese scholars. We can argue that these
trends will continue and influence the subsequent disinformation research.
Another corroboration of the rise of emerging power is the rise of emerging academic
journals. As we can see, journals such as the International Journal of Environmental
Research and Public Health and Vaccines of MDPI Press are becoming more and more
important platforms in disinformation research, and the territory of traditional publishers
such as Elsevier and Springer is shrinking step by step.
Another trend in the future of disinformation research is that research forces are
becoming increasingly diverse and the research vision will become broader. We can see
that many of the highly productive authors in this field are not from major countries and
institutions. At the same time, there is not a close collaborative chain among many authors.
The analysis of the research areas further illustrates the broad field and disciplinary span
of disinformation research. The 5666 papers involve 617 research fields, among which the
most published papers belong to interdisciplinary fields such as public health, computer
science, engineering, and policy. In addition, attention to the research of disinformation has
also been paid in other interdisciplinary fields such as agricultural economy, food science,
electrical engineering, and chemistry.
With regard to research topics, infodemic public health related to COVID-19 and
the application of big data technology in disinformation are hot issues in disinformation
research. In the past three years, the number of relevant papers has increased sharply,
which is also confirmed by the increasing development trend of derived keywords such as
infodemic and COVID-19 in keyword analysis.
Although disinformation research has achieved considerable success in many aspects,
the existing research results indicate that more in-depth research should be conducted in
the following directions to meet the crushing challenges posed by disinformation.
First, the typical formation process of disinformation has obvious stage-specific charac-
teristics. How to sensitively perceive and distinguish the stages in which the disinformation
Int. J. Environ. Res. Public Health 2022, 19, 16849 17 of 21
is located and analyze the evolution mechanism of sub-stages is a topic that needs further
research. This requires not only more involvement and extensive collaboration from schol-
ars in the fields of information management, compute science, social management, and
other disciplines, but perhaps also a new theory to provide theoretical support.
Second, according to the studies of scholars, social media—represented by Twitter—
has become a breeding ground for disinformation. It is more meaningful to analyze the
causes of disinformation from the perspective of social media users, such as user profiles of
vulnerable groups in social media and behavioral tracking of high-impact users.
Finally, AI technologies facilitate the triggering and proliferation of disinformation,
such as Deepfake and Botnets. It becomes crucial to make AI technologies serve the
governance of disinformation. For example, optimization algorithms are used to establish
social media information source-awareness mechanisms, detection algorithms are used
to screen fake information, and algorithmic transparency is enhanced to improve users’
ability to distinguish information.
5. Conclusions
This study shows the research overview of disinformation from 2002 to 2021. Based
on the bibliometric analysis, this paper shows the distribution of global disinformation
research, analyzes the differences and connections among countries/regions, core authors,
and research institutions, and outlines the four hot topics of disinformation, being group
heterogeneity of misinformation in memory, disinformation mechanism in social media,
public health related to COVID-19, and application of big data technology in disinformation.
This study can help scholars in the field of information-related research quickly grasp the
full picture of disinformation in global research, and help researchers understand the
current research results in this field to carry out more in-depth research.
Author Contributions:
Conceptualization, methodology and software, Y.J.; writing—original draft
preparation and visualization, F.S.; writing—review and editing and supervision, S.W.; writing—
review and editing, L.Y. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Natural Science Foundation of China, grant
number: No. 71901195, and Hangzhou Philosophy and Social Science Planning Project, grant number
No. M22JC104.
Institutional Review Board Statement:
Not applicable, the study did not involve humans or animals.
Informed Consent Statement: Not applicable, the study did not involve humans.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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